---
title: Knowledge Base Architecture
description: Deep dive into knowledge base design, architecture, and how they're optimized for AI agent retrieval.
---

Knowledge bases in Agno are architecturally designed for AI agent retrieval, with specialized components that bridge the gap between raw data storage and intelligent information access.

## Knowledge Base Components

Knowledge bases consist of several interconnected layers that work together to optimize information for agent retrieval:

### Storage Layer
**Vector Database**: Stores processed content as embeddings optimized for similarity search
- PgVector for production scalability
- LanceDB for development and testing
- Pinecone for managed cloud deployments

### Processing Layer
**Content Pipeline**: Transforms raw information into searchable format
- Readers parse different file types
- Chunking strategies break content into optimal pieces
- Embedders convert text to vector representations

### Access Layer
**Search Interface**: Enables intelligent information retrieval
- Semantic similarity search
- Hybrid search combining vector and keyword matching
- Metadata filtering for precise results

## How Agents Use Knowledge Bases

When you give an agent access to a knowledge base, several powerful capabilities emerge:

### Automatic Information Retrieval
The agent doesn't need to be told when to search - it automatically determines when additional information would help answer a question or complete a task.
Although - explicitly instructing the agent to search a knowledge base is a perfectly fine and very common use case. 

```python
# Agent automatically searches when needed
user: "What's our current return policy?"
# Agent searches knowledge base for return policy information
# Agent responds with current, accurate policy details
```

### Contextual Understanding
The agent understands the context of questions and searches for the most relevant information, not just keyword matches.

```python
# Understands intent and context
user: "Can I send back this item I bought last month?"
# Searches for: return policies, time limits, return procedures
# Not just: "send back", "item", "bought", "month"
```

### Source Attribution
Agents can provide references to where they found information, building trust and enabling verification.

```python
# Response includes source information
"Based on section 3.2 of our Return Policy document,
items can be returned within 30 days of purchase..."
```

## Knowledge Base Architecture

Here's how the different pieces work together:

<Steps>
  <Step title="Content Ingestion">
    Raw content is processed through **readers** that understand different file formats (PDF, websites, databases, etc.) and extract meaningful text.
  </Step>
  <Step title="Intelligent Chunking">
    Large documents are broken down into smaller, meaningful pieces using **chunking strategies** that preserve context while enabling precise retrieval.
  </Step>
  <Step title="Embedding Generation">
    Each chunk is converted into a vector embedding that captures its semantic meaning using **embedders** powered by language models.
  </Step>
  <Step title="Vector Storage">
    Embeddings are stored in **vector databases** optimized for similarity search, often with support for hybrid search combining semantic and keyword matching.
  </Step>
  <Step title="Intelligent Retrieval">
    When agents need information, they generate search queries, find similar embeddings, and retrieve the most relevant content chunks.
  </Step>
</Steps>

## Benefits of Knowledge-Powered Agents

### Accuracy and Reliability
- Responses are grounded in your specific information, not generic training data
- Reduced hallucinations because agents reference actual sources
- Up-to-date information that reflects your current state

### Scalability and Maintenance
- Add new information without retraining or modifying code
- Handle unlimited amounts of information without performance degradation
- Easy updates by simply adding new content to the knowledge base

### Context Awareness
- Agents understand your specific domain, terminology, and processes
- Responses are tailored to your organization's context and needs
- Consistent information across all agent interactions

## Getting Started with Knowledge Bases

Ready to build your own knowledge base? The process is straightforward:

<CodeGroup>
```python Basic Setup
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.lancedb import LanceDb

# Create a knowledge base
knowledge = Knowledge(
    vector_db=LanceDb(table_name="my_knowledge")
)

# Add your content
knowledge.add_content(path="documents/")
```

```python With Custom Configuration
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.embedder.openai import OpenAIEmbedder

# Customized knowledge base
knowledge = Knowledge(
    vector_db=PgVector(
        table_name="company_knowledge",
        embedder=OpenAIEmbedder(model="text-embedding-3-large")
    )
)
```
</CodeGroup>

## Learn More

<CardGroup cols={2}>
  <Card title="Content Types" icon="file-lines" href="/concepts/knowledge/content_types">
    Explore different ways to add information to your knowledge base
  </Card>
  <Card title="Search & Retrieval" icon="magnifying-glass" href="/concepts/knowledge/core-concepts/search-retrieval">
    Learn how agents search and find relevant information
  </Card>
  <Card title="Vector Databases" icon="database" href="/concepts/vectordb/overview">
    Choose the right storage solution for your knowledge base
  </Card>
  <Card title="Performance Tips" icon="gauge" href="/concepts/knowledge/advanced/performance-tips">
    Optimize your knowledge base for speed and accuracy
  </Card>
</CardGroup>